{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:15:43Z","timestamp":1743117343282,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811692468"},{"type":"electronic","value":"9789811692475"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-9247-5_24","type":"book-chapter","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T21:25:33Z","timestamp":1641936333000},"page":"306-314","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Discussion of Data Sampling Strategies for Early Action Prediction"],"prefix":"10.1007","author":[{"given":"Xiaofa","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianqin","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Kong, Y., Tao, Z., Fu, Y.: Deep sequential context networks for action prediction, In: CVPR, 2017, pp. 1473\u20131481 (2017)","DOI":"10.1109\/CVPR.2017.390"},{"key":"24_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/978-3-030-01219-9_26","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L Chen","year":"2018","unstructured":"Chen, L., Lu, J., Song, Z., Zhou, J.: Part-activated deep reinforcement learning for action prediction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 435\u2013451. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_26"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Liu, J., Shahroudy, A., Wang, G., Duan, L.Y., Kot, A.C.: SSNet: scale selection network for online 3D action prediction, In: CVPR, pp. 8349\u20138358 (2018)","DOI":"10.1109\/CVPR.2018.00871"},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Zhao, H., Wildes, R.P.: Spatiotemporal feature residual propagation for action prediction, In: ICCV, pp. 7003\u20137012 (2019)","DOI":"10.1109\/ICCV.2019.00710"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Gammulle, H., Denman, S., Sridharan, S., Fookes, C.: Predicting the future: a jointly learnt model for action anticipation, In: ICCV, pp. 5562\u20135571 (2019)","DOI":"10.1109\/ICCV.2019.00566"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Wang, X., Hu, J. F., Lai, J. H., Zhang, J., Zheng, W.S.: Progressive teacher-student learning for early action prediction, In: CVPR, pp. 3556\u20133565 (2019)","DOI":"10.1109\/CVPR.2019.00367"},{"key":"24_CR7","unstructured":"Scarafoni, D., Essa, I., Ploetz, T.: PLAN-B: predicting likely alternative next best sequences for action prediction, arXiv preprint arXiv:2103.15987 (2021)"},{"key":"24_CR8","unstructured":"Alexander K., Marcin M., Cordelia S.: A spatio-temporal descriptor based on 3d-gradients, In: British Machine Vision Conference, pp. 275\u2013281 (2008)"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition, In: ACM International Conference on Multimedia, pp. 357\u2013360 (2007)","DOI":"10.1145\/1291233.1291311"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset, In: CVPR, pp. 4724\u20134733 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3d residual networks for action recognition, In: ICCV, p. 4 (2017)","DOI":"10.1109\/ICCVW.2017.373"},{"key":"24_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-319-46484-8_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"L Wang","year":"2016","unstructured":"Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20\u201336. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_2"},{"key":"24_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/978-3-030-20893-6_23","volume-title":"Computer Vision \u2013 ACCV 2018","author":"Y Zhu","year":"2019","unstructured":"Zhu, Y., Lan, Z., Newsam, S., Hauptmann, A.: Hidden two-stream convolutional networks for action recognition. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 363\u2013378. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_23"},{"key":"24_CR14","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos, In: NIPS, pp. 568\u2013576 (2014)"},{"key":"24_CR15","unstructured":"Soomro, K., Zamir, A.R., Shah, M.: Ucf101: a dataset of 101 human actions classes from videos in the wild, CoRR (2012). abs\/1212.0402"},{"key":"24_CR16","unstructured":"Kay, W., et al.: The kinetics human action video dataset, CoRR (2017). arXiv preprint arXiv:1705.06950"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks, In: ICCV, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Yao, T., Mei, T.: Learning spatio-temporal representation with pseudo-3d residual networks, In: ICCV, pp. 5533\u20135541 (2017)","DOI":"10.1109\/ICCV.2017.590"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., et al.: SlowFast networks for video recognition, In: ICCV, pp. 6202\u20136211 (2019)","DOI":"10.1109\/ICCV.2019.00630"},{"key":"24_CR20","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning. PMLR, pp. 448\u2013456 (2015)"}],"container-title":["Communications in Computer and Information Science","Cognitive Systems and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-9247-5_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T02:15:58Z","timestamp":1651889758000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-9247-5_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811692468","9789811692475"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-9247-5_24","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cognitive Systems and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Suzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccsip2021.tsingzhan.com\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}